Snorkel AI

OCTOBER 4, 2023

Instead of the rule-based decision-making of traditional credit scoring, AI can continually learn and adapt, improving accuracy and efficiency. Data teams can fine-tune LLMs like BERT, GPT-3.5 See what Snorkel can do to accelerate your data science and machine learning teams. Book a demo today.

AI 64

AI AI Data Scientist Neural Network 64

Snorkel AI

OCTOBER 4, 2023

Instead of the rule-based decision-making of traditional credit scoring, AI can continually learn and adapt, improving accuracy and efficiency. Data teams can fine-tune LLMs like BERT, GPT-3.5 Learn more See what Snorkel can do to accelerate your data science and machine learning teams. Book a demo today.

AI 59

AI AI Data Scientist Neural Network 59

Viso.ai

APRIL 4, 2024

Find out how Viso Suite can automate your team’s projects by booking a demo. Continuous Learning and Adaptive Models: Online learning continuously updates the model as new data becomes available. On the other hand, transfer learning may help by adapting the model trained to do one task to do a related task.

Data Drift 52

Data Drift Computer Vision Machine Learning Algorithm 52

Viso.ai

MARCH 26, 2024

Learn more by booking a demo. Source ) This has led to groundbreaking models like GPT for generative tasks and BERT for understanding context in Natural Language Processing ( NLP ). As research progresses, attention mechanisms will further enhance the capabilities and interpretability of deep learning models.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Viso.ai

JANUARY 2, 2024

Therefore, they are fed billions of words from books, articles, websites, social media (X, Facebook, Reddit), and more. Large language models learn language patterns, grammar, facts, and even writing styles from this diverse input. BERT, LaMDA, Claude 2, etc. Alternatives include ChatGPT 4.0,

Large Language Models 52

Large Language Models Deep Learning Computer Vision LLM 52

Sebastian Ruder

DECEMBER 6, 2021

Reading Comprehension assumes a gold paragraph is provided Standard approaches for reading comprehension build on pre-trained models such as BERT. Using BERT for reading comprehension involves fine-tuning it to predict a) whether a question is answerable and b) whether each token is the start and end of an answer span.

BERT 52

BERT NLP Natural Language Processing Computational Linguistics 52